R Libraries for Remote Sensing Data Classification by k-means Clustering and NDVI Computation in Congo River Basin, DRC

Polina Lemenkova () and Olivier Debeir

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

Abstract: In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

Keywords: image processing; remote sensing; Landsat; R language; programming; cartography; mapping; data visualization; NDVI; Africa (search for similar items in EconPapers)
JEL-codes: C61 N57 O13 O44 Q01 Q20 Q23 Q24 Q51 Q55 R11 Y91 (search for similar items in EconPapers)
Date: 2022-12-07
New Economics Papers: this item is included in nep-big
Note: SCOPUS: ar.j
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in: Applied Sciences (Switzerland) (2022) v.12 n° 24,p.1-26

Downloads: (external link)
https://dipot.ulb.ac.be/dspace/bitstream/2013/352357/3/applsci-12-12554-v2.pdf Full text for the whole work, or for a work part (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ulb:ulbeco:2013/352357

Ordering information: This working paper can be ordered from
http://hdl.handle.ne ... lb.ac.be:2013/352357

Access Statistics for this paper

More papers in ULB Institutional Repository from ULB -- Universite Libre de Bruxelles Contact information at EDIRC.
Bibliographic data for series maintained by Benoit Pauwels ().